Then in late November, as part of a broader effort to broaden and deepen public dialogue about science and AI, we co-hosted the AI for Science Forum with the Royal Society, which convened scientists, researchers, government leaders and managers to discuss key topics. such as solving the challenge of predicting protein structure, mapping the human brain and saving lives by accurately predicting and detecting wildfires. We held a Q&A with four Nobel laureates on this forum, Sir Paul Nurse, Jennifer Doudna, Demis Hassabis and John Jumper, available to listen to on the Google DeepMind podcast.
This was also a landmark year for another reason: Demis Hassabis and John Jumper, along with David Baker, were awarded the 2024 Nobel Prize® in Chemistry for their work on AlphaFold 2. As the Nobel committee recognized, the work theirs:
“[H]as completely new possibilities for protein design have opened up that have never been seen before, and we now have access to the predicted structures of all 200 million known proteins. These are really big achievements.”
It was also great to see the 2024 Nobel Prize® in Physics awarded to long-retired Googler Geoffrey Hinton (and John Hopfield), “for fundamental discoveries and inventions that enable machine learning through artificial neural networks.”
The Nobels followed additional Google recognitions including the NeurIPS 2024 Test of Time Paper Awards for Sequence to Sequence Learning with Neural Networks and Generative Adversarial Nets, and the Beale—Orchard-Hays Prize, awarded to a joint team of Google educators and experts. for important work on Primal-Dual Linear Programming (PDLP). (PDLP, now part of Google OR Tools, helps solve big linear programming problems for real-world applications from data center network traffic engineering to container deployment optimization.)
AI for the benefit of humanity
This year, we made a number of product developments and published research that shows how AI can benefit people directly and quickly, from preventive and diagnostic medicine to disaster preparedness and recovery to learning.
In healthcare, AI holds the promise of democratizing quality of care in key areas, such as early detection of heart disease. Our research has shown that using a simple finger device that measures blood flow variability, combined with basic metadata, can predict cardiovascular health risks. Building on previous AI-enabled TB diagnosis research, we show how AI models can be used for accurate TB screening in people with high rates of TB and HIV. This is important in reducing the spread of TB (more than 10 million people get sick from it each year), as about 40 percent of people with TB go undiagnosed.
